Tesla ML Interview Question: Cross-Validation Techniques

This question asks you to explain and compare cross-validation methods used for model evaluation and selection, focusing on practical trade-offs and when to apply each technique.

You should be able to describe core methods (k-fold CV, leave-one-out (LOOCV)), their step-by-step mechanics, and variants such as stratified k-fold and time-series (rolling) CV. Explain how each method partitions data, how model training and validation are repeated, and how results are aggregated (e.g., mean and confidence intervals of metrics).

Interview flow often starts with basic definitions and motivation (why cross-validation is used versus a single train/validation split), then moves to comparisons: bias vs variance of estimators, computational cost (LOOCV vs k-fold), and practical rules for choosing k. You may be asked to pick a method for different scenarios (small dataset, imbalanced classes, temporal data) and to justify your choice.

Skill signals the interviewer looks for: solid understanding of the bias–variance trade-off, ability to explain stratification and time-aware validation, familiarity with nested cross-validation for hyperparameter tuning, awareness of computational limitations and alternatives (e.g., bootstrap), and ability to translate theory into practice (code-level considerations, reproducibility, and performance estimation). Be ready to propose evaluation pipelines, discuss metric selection, and answer follow-ups about nested CV and variance estimation.